Inpatient Glycemic Control Research Articles

Construction and preliminary evaluation of the inpatient glycemic control questionnaire (IGCQ): a survey tool assessing perceptions and knowledge of resident physicians

BackgroundUncontrolled hyperglycemia in hospitalized patients, with or without diabetes mellitus, is associated with many adverse outcomes. Resident physicians are the primary managers of inpatient glycemic control (IGC) in many academic and community medical centers; however, no validated survey tools related to their perceptions and knowledge of IGC are currently available. As identification of common barriers to successful IGC amongst resident physicians may help foster better educational interventions (ultimately leading to improvements in IGC and patient care), we sought to construct and preliminarily evaluate such a survey tool.MethodsWe developed the IGC questionnaire (IGCQ) by using previously published but unvalidated survey tools related to physician perspectives on inpatient glycemic control as a framework. We administered the IGCQ to a cohort of resident physicians from the University of Mississippi Medical Center, University of Louisville, Emory University, and the University of Virginia. We then used classical test theory and Rasch Partial Credit Model analyses to preliminarily evaluate and revise the IGCQ. The final survey tool contains 16 total items and three answer-choice categories for most items.ResultsTwo hundred forty-six of 438 (56.2%) eligible resident physicians completed the IGCQ during various phases of development.ConclusionsWe constructed and preliminarily evaluated the IGCQ, a survey tool that may be useful for future research into resident physician perceptions and knowledge of IGC. Future studies could seek to externally validate the IGCQ and then utilize the survey tool in pre- and post-intervention assessments.

Development and Evaluation of Predictive Models Designed to Drive an Intelligent Clinical Decision Support System for Optimizing Inpatient Glycemic Control

Background: Achieving glycemic control in critically ill patients is of paramount importance, and has been linked to reductions in mortality, ICU length of stay (LOS), and morbidities such as infection, regardless whether or not the patient has diabetes. The myriad of illnesses and patient conditions render maintenance of glycemic control very challenging in this setting. Methods: This study involved collection of continuous glucose monitoring (CGM) data, and other associated measures, from the electronic medical records of 127 patients for the first 72 hours of ICU admission at The Ohio State University Wexner Medical Center (OSWMC). Data collected was used to develop a neural network-based predictive models. Findings: The model was shown to be accurate in predicting glucose using patient data not leveraged for model training. Model error, expressed as mean absolute difference percent, was 10.6% and 15.9% with respect to interstitial and serum blood glucose values, respectively. Clarke Error Grid Analysis (CEGA) of model predictions with respect to the reference CGM and blood glucose measurements revealed that >99% of model predictions could be regarded as clinically acceptable. Interpretation: The noted clinical acceptability of these models illustrates their potential utility within a clinical decision support system to assist healthcare providers in the optimization of glycemic management in critical care patients. Funding Statement: This study was supported by a $98,366 grant provided by the State of Ohio's Third Frontier Program (Grant #TECG20140125) and $20,000 provided by the Department of Anesthesiology at OSUWMC. Declaration of Interests: Dr. Jaume has nothing to disclose. Mr. Owais has nothing to disclose. Dr. Papadimos has nothing to disclose. Dr. Tripathi has nothing to disclose. Dr. Mavarez Martinez has nothing to disclose. Dr. Cameron has received one initial payment from the University of Toledo that was due to an initial licensing fee paid by Analytic Diabetic Systems, Inc. as part of licensing the patents referenced above as part of potential future commercial product development of a clinical decision support system. Dr. Pappada reports grants and personal fees from Analytic Diabetic Systems, Inc., personal fees from University of Toledo, during the conduct of the study; In addition, Drs. Pappada and Cameron hold patents (U.S. Patent No. 8,762,306 B2 and U.S. Patent No. 9,076,107 B2) related to the GlyCU CDSS with royalties paid to the University of Toledo and Co-Inventors (Drs. Pappada and Cameron) from Analytic Diabetic Systems, Inc. Dr. Pappada is on the Board of Directors for Analytic Diabetic Systems, Inc. and is a Co-Founder of the company, and holds an equity position in the corporation. Ethics Approval Statement: The study protocol was approved by The Ohio State University Institutional Review Board. All patients or their next of kin (if the patient had lost decision-making capacity) provided written informed consent.

1268-P: Evaluation of Inpatient Insulin Dose Requirements and Glycemic Control of Patients Using U-500 Regular Insulin Prior to Admission

Objective: Questions remain regarding the appropriate use of U-500 insulin particularly, in the hospital setting. The objective of this study was to evaluate the inpatient insulin dose requirements and glycemic control of patients using U-500 insulin prior to admission. Methods: A retrospective chart review of adult patients using U-500 insulin prior to admission to Beaumont Hospitals between March 2012 and July 2017 was conducted. Inpatient insulin dose requirements and glycemic control were compared between those who were continued on U-500 insulin and those transitioned to a U-100 insulin regimen. Results: A total of 95 patients were included in the study. Seventeen patients were continued on U-500 insulin while the remaining 78 received U-100 insulin during their inpatient stay. U-500 insulin group received significantly more units of insulin per day (181 units versus 99.65 units; p = 0.0048). However, the proportion of insulin administered inpatient relative to the prior to admission dose was similar between the two groups (59% in the U-500 group and 46% in U-100 group; p = 0.1344). Glycemic control was similar between the groups except for significantly more hypoglycemia events in the U-500 group compared to the U-100 group (p < 0.0001). When patients were further stratified by prior to admission dose or HbA1c, significantly more hypoglycemia was observed in the U-500 group with HbA1c < 8% and those with prior to admission dose between 300-600 units. Conclusions: Our retrospective review demonstrated that only very few patients were continued on a U-500 insulin regimen during their hospitalization. Inpatient use of U-500 insulin posed higher risk of hypoglycemia even when used at much lower doses than the established home regimen. Continued use of U-500 insulin in the hospital requires careful assessment of the risks and further investigation is needed to determine the appropriate dosing. Disclosure P. Sanaty Zadeh: None. M.F. Lutz: None. A.A. Feleke: None.

649-P: Making Glucose the Next Vital Sign: An Educational Intervention to Teach Clinicians How to Improve Inpatient Glycemic Control

Background: Although the American Diabetes Association recommends avoiding hypoglycemia and hyperglycemia in the inpatient setting, there is a lack of comprehensive education offered to clinicians to learn steps to achieve these goals. Objectives: 1) Decrease rates of malglycemia while increasing percent of patients in target BG ranges by 3% on medicine units. 2) Increase prescriber test scores post educational intervention. Methods: We developed interactive role specific educational interventions for physicians, nurse practitioners, physician assistants and registered nurses focusing on strategies to improve glycemic control while preventing adverse events. Education was tailored to each profession with case based face-to-face and on-line educational modules. Glycemic Management pocket cards and dosing guidelines for steroids, enteral feedings and preparation for surgery were distributed as well as "Glucose: The Next Vital Sign" Buttons to create a culture of safety and shared vision. Conclusion: Results to date indicate an improvement in target BG readings. At the end of February 2019, we will calculate overall rates of hypoglycemia and hyperglycemia as well as in target BGs for the 6 month period and compare to the same months one year earlier. We will analyze prescriber and RN test scores, revise educational intervention and offer the program to other service lines. Disclosure J. Seley: Advisory Panel; Self; AstraZeneca, Sanofi US. Consultant; Self; Ascensia Diabetes Care, Johnson & Johnson Diabetes Institute. F.A. Mendelsohn Curanaj: None. T. Yeh: None. M. Yeung: None. J. Falcone: None. Funding Weill Cornell Medicine

Impact of Measurement Error on Predicting Population-Based Inpatient Glucose Control

Aim:Instrument measurement error (ME) may affect ability of damped trend analysis to forecast inpatient glycemic control.Materials & methods:A statistical approach was developed to introduce ME into damped trend analysis algorithm. Point-of-care blood glucose device data were extracted from the laboratory system. Forecasts were generated from various inpatient subgroups and time intervals.Results:ME produced differences in damped trend model during the forecast learning cycle. However, forecast trajectory stayed identical regardless of ME in 85% (119/140) of studied scenarios. Forecasts did not change with greater ME.Conclusion:ME inherent in the point-of-care blood glucose device had little effect on trajectory of damped trend exponential forecasts and apparently would not influence decision making in inpatient glycemic control algorithms.

Glucose Management Technologies for the Critically Ill.

Hyperglycemia is common in the intensive care unit (ICU) both in patients with and without a previous diagnosis of diabetes. The optimal glucose range in the ICU population is still a matter of debate. Given the risk of hypoglycemia associated with intensive insulin therapy, current recommendations include treating hyperglycemia after two consecutive glucose >180 mg/dL with target levels of 140-180 mg/dL for most patients. The optimal method of sampling glucose and delivery of insulin in critically ill patients remains elusive. While point of care glucose meters are not consistently accurate and have to be used with caution, continuous glucose monitoring (CGM) is not standard of care, nor is it generally recommended for inpatient use. Intravenous insulin therapy using paper or electronic protocols remains the preferred approach for critically ill patients. The advent of new technologies, such as electronic glucose management, CGM, and closed-loop systems, promises to improve inpatient glycemic control in the critically ill with lower rates of hypoglycemia.

Dietary Intervention for Inpatient Glycemic Control

While recommendations for inpatient glycemic control suggest a conservative target of 140-180mg/dL, many struggle to achieve this goal. Glycemic control begins with a carbohydrate-controlled diet, but many inpatient diets continue to be suboptimal and can contribute to hyperglycemia. At our institution, the carbohydrate-controlled breakfast diet was changed to better reflect carbohydrate goals (e.g., replacing french toast on the “bad diet” with vegetable frittatas on the “good diet”). Pre- and post-prandial glucose values for patients with types I and II diabetes receiving a carbohydrate-controlled diet were collected by retrospective chart review during the weeks before and after these changes took effect. Patients on steroids were analyzed separately. Individual post-prandial glucose values were recorded in a binary fashion as meeting or exceeding predefined glucose targets (<180 mg/dL, <250mg/dL, and <350mg/dL). During this period, 369 patients not receiving steroids and 39 patients receiving steroids had at least one relevant glucose value. In the non-steroid group, there was no difference in proportion of patients who exceeded any of the predefined thresholds. However, in the steroid group, there was a trend toward reduction in proportion of patients exceeding blood glucose of 180mg/dL (absolute risk reduction 19%, 95% confidence interval -4% to 42%, p = 0.12) and 250mg/dL (absolute risk reduction 17%, 95% confidence interval -2% to 35%, p = 0.08) on the days after the dietary change. Although all patients with diabetes may experience hyperglycemia proportionate to their carbohydrate intake, those on steroids experience induced insulin resistance leading to especially diet-sensitive glucose excursions. Our data suggests that patients receiving steroids can likely achieve improved inpatient glucose control through dietary modification. Physician and administration attention to carbohydrate-controlled menus represents an opportunity for improved quality of care for inpatients with diabetes. Disclosure S. Cromer: None. M.M. Bogun: Other Relationship; Self; Mytonomy.

An Innovative Cost-Effective Model of Using Advanced Care Practitioners (ACPs) in Improving Glycemic Control and LOS in a Large Hospital System

In 2001 healthcare systems identified issues related to increased length of stay and suboptimal outcomes associated with poor inpatient glycemic control. Diabetes clinical initiative (DCI) team was created and the physician champion developed protocols and restructured the delivery of diabetic care across the healthcare system. However it was difficult to provide uninterrupted service due to shortage of Endocrinologists. An innovative care model was designed in 2009 using specialty trained Endocrinology ACPs’ to improve Glycemic control across system and to provide uninterrupted service including weekends. The ACP’s were trained by the physician champion both with didactic teachings and ongoing case discussions. Automatic consults to ACPS’ were initiated for specific criteria identified by lead physicians .These criteria included HbA1C more than 8.5,Blood Glucose (BG) greater than 200 X4 in 24 hours, BG greater than 300 x2 in 24 hours and Hypoglycemia less than 40x2 in 24 hours .The ACPs’ were always supported by the endocrinology team . For the last 8 years the glycemic control across the hospital has further improved from average blood glucose of 160-153mg/dl. The rate of mild hypoglycemia remains low at 2% and severe hypoglycemia at < 0.4% . This has resulted in continuation of improved LOS and patient satisfaction as well. Much improved results in Coronary artery bypass surgery (CABG) population have been shown with no deep sternal wound infections in last 6 years. The model remains cost effective for the whole system. Summary: Innovative model of Glycemic control using specialty trained ACPS’ is cost effective and improves glycemic control and LOS. The ongoing education and support is critical to the success of the program. Disclosure R. Joshi: None. A. Helmuth: None.

Inpatient Glycemic Management of Non-cardiac CVD: Focus on Stroke and PVD

Hyperglycemia occurs frequently in hospitalized patients with stroke and peripheral vascular disease (PVD). Guidelines for inpatient glycemic management are not well established for this patient population. We will review the clinical impact of hyperglycemia in this acute setting and review the evidence for glycemic control. Hyperglycemia in acute stroke is associated with poor short and long-term outcomes, and perioperative hyperglycemia in those undergoing revascularization for PVD is linked to increased post-surgical complications. Studies evaluating tight glucose control do not demonstrate improvement in clinical outcomes, although the risk for hypoglycemia increases substantially. Additional studies are needed to evaluate tight glucose goals relative to our current standard of care and the role of permissive hyperglycemia. Given the limited data to guide glycemic management in these patient populations, it is recommended that general guidelines for inpatient glycemic control be followed. Special considerations should be made to address factors that may impact glucose management, including neurological deficits and clinical changes that occur in the postoperative state.

Bridging technology and clinical practice: innovating inpatient hyperglycaemia management in non-critical care settings.

Emerging evidence shows that suboptimal glycaemic control is associated with increased morbidity and length of stay in hospital. Various guidelines for safe and effective inpatient glycaemic control in the non-critical care setting have been published. In spite of this, implementation in practice remains limited because of the increasing number of people with diabetes admitted to hospital and staff work burden. The use of technology in the outpatient setting has led to improved glycaemic outcomes and quality of life for people with diabetes. There remains an unmet need for technology utilisation in inpatient hyperglycaemia management in the non-critical care setting. Novel technologies have the potential to provide benefits in diabetes care in hospital by improving efficacy, safety and efficiency. Rapid analysis of glucose measurements by point-of-care devices help facilitate clinical decision-making and therapy adjustment in the hospital setting. Glucose treatment data integration with computerized glucose management systems underpins the effective use of decision support systems and may streamline clinical staff workflow. Continuous glucose monitoring and automation of insulin delivery through closed-loop systems may provide a safe and efficacious tool for hospital staff to manage inpatient hyperglycaemia whilst reducing staff workload. This review summarizes the evidence with regard to technological methods to manage inpatient glycaemic control, their limitations and the future outlook, as well as potential strategies by healthcare organizations such as the National Health Service to mediate the adoption, procurement and use of diabetes technologies in the hospital setting.

Quantifying the Effect of Diabetes on Surgical Hand and Forearm Infections.

Diabetes has long been established as a risk factor for hand and forearm infections. The purpose of this study was to review the effect of glycemic factors on outcomes among diabetic patients with surgical upper-extremity infections. We hypothesized that diabetic inpatients may benefit from stronger peri-infection glycemic control. A prospective cohort study enrolled diabetic and nondiabetic surgical hand and forearm infections over 3 years. Glycemic factors included baseline glycosylated hemoglobin, blood glucose (BG) at presentation, and inpatient BG. Poor baseline control was defined as glycosylated hemoglobin of 9.0% or greater and poor inpatient control as average BG of 180 mg/dL or greater. The main outcome of interest was the need for repeat therapeutic drainage. Multivariable logistic regression quantified the association between diabetic factors and this outcome. The study involved 322 patients: 76 diabetic and 246 nondiabetic. Diabetic infections were more likely than nondiabetic infections to result from idiopathic mechanisms, occur in the forearm, and present as osteomyelitis, septic arthritis, and necrotizing fasciitis. Diabetic microbiology was more likely polymicrobial and fungal. After first drainage, diabetic patients were more likely to require repeat drainage and undergo eventual amputation. Among diabetic patients, poor inpatient control was associated with need for repeat drainage. Diabetes exacerbates the burden of surgical upper-extremity infections: specifically, more proximal locations, deeper involved anatomy at presentation, broader pathogenic microbiology, increased need for repeat drainage, and higher risk for amputation. Among diabetic patients, poor inpatient glycemic control is associated with increased need for repeat drainage. Prognostic I.

Glucometrics and Insulinometrics.

Glucometrics is the systematic analysis of inpatient glucose data and is of key interest as hospitals strive to improve inpatient glycemic control. Insulinometrics is the systematic analysis and reporting of inpatient insulin therapy. This paper reviews some of the questions to be resolved before a national benchmarking process can be developed that will allow institutions to track and compare inpatient glucose control performance against established guidelines. There remains a lack of standardization on how glucometircs should be measured and reported. Before hospitals can commit resources to compiling and extracting data, consensus must be reached on such questions as which measures to report, definitions of glycemic targets, and how data should be obtained. Examples are provided on how insulin administration can be measured and reported. Hospitals should begin assessment of glucometrics and insulinometrics. However, consensus and standardization must first occur to allow for a national benchmarking process.

How is the weather? Forecasting inpatient glycemic control.

Aim:Apply methods of damped trend analysis to forecast inpatient glycemic control.Method:Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting.Results:The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries.Conclusion:Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement.

Comparison of Efficacy and Safety of Glargine and Detemir Insulin in the Management of Inpatient Hyperglycemia and Diabetes

Glargine and detemir insulin are the two most commonly prescribed basal insulin analogues for the ambulatory and inpatient management of diabetes. The efficacy and safety of basal insulin analogues in the hospital setting has not been established. This observational study compared differences in glycemic control and outcomes in non-intensive care unit patients with blood glucose (BG) >140 mg/dL who were treated with glargine or detemir, between January 1, 2012, and September 30, 2015, in two academic centers. Among 6,245 medical and surgical patients with hyperglycemia, 5,749 received one or more doses of glargine, and 496 patients received detemir during the hospital stay. There were no differences in the mean daily BG (glargine, 182 ± 46 mg/dL vs. detemir, 180 ± 44 mg/dL; P = .70). There were no differences in mortality, hospital complications, or re-admissions between groups (all, P>.05). After adjusting for potential confounders, there was no statistically significant difference in hypoglycemia rates between treatment groups. Patients treated with detemir required higher total daily basal insulin doses (0.27 ± 0.16 units/kg/day vs. 0.22 ± 0.15 units/kg/day; P<.001). Glargine-treated patients had statistically longer length of stay; however, this difference may not be clinically relevant (6.8 ± 7.4 days vs. 6.0 ± 6.3 days; P<.001). Our study indicates that treatment with glargine and detemir results in similar inpatient glycemic control in general medicine and surgery patients. Detemir treatment was associated with higher daily basal insulin dose and number of injections. A prospective randomized study is needed to confirm these findings. BG = blood glucose BMI = body mass index CI = confidence interval eGFR = estimated glomerular filtration rate HbA1c = glycated hemoglobin ICD-9 = International Classification of Diseases, ninth revision ICU = intensive care unit IQR = interquartile range LOS = length-of-stay OR = odd ratio.

Inpatient glucose management programme in the Asian healthcare setting.

To implement an inpatient glucose management (IGM) programme in the general medical wards and evaluate its clinical efficacy. Consecutive patients admitted to selected medical wards over a 12-month period were included in the IGM programme. All patients with ≥3 capillary blood glucose (CBG) readings (>10.0mmol/L and/or <4.0mmol/L) over a 24-hour period were identified electronically. A multidisciplinary team of diabetes care providers would provide specialist input to these patients. The control group comprised of consecutive patients admitted over the preceding 12months. Outcome glucose measures include mean in-hospital glucose (MHG), mean patient-day glucose (MDG), proportion of CBG readings at predefined cut-offs and length of stay (LOS). Both the MHG and MDG were significantly lower following intervention (10.0±2.4mmol/L vs 11.2±2.6mmol/L, P<.001; 10.0±2.3mmol/L vs 11.2±2.6mmol/L, P<.001, respectively). Prevalence of hyperglycaemic events, defined by CBG >10.0mmol/L, was significantly lower at 36.5% versus 51.6% (P<.001). Hypoglycaemic events of CBG <4.0mmol/L remained infrequent at <1.0% before and after IGM programme. A greater proportion of glucose readings was controlled within the target range of 4.0-10.0mmol/L (62.6% vs 47.6%, P<.001). With the IGM programme in place, more patients received scheduled CBG monitoring, and a significant shortening of mean LOS by 3.2days was observed (P=.02). The IGM programme was effective in improving inpatient glycaemic monitoring and control in the general medical wards, with a significant reduction in LOS observed. These demonstrated the programme's potential to enhance quality and efficiency of patient care.

Associations between home insulin dose adjustments and glycemic outcomes at hospital admission.

To describe patterns of home insulin dose adjustments for non-surgical, non-critically ill patients at admission and to describe associations between these adjustments and inpatient glycemic control. Hospital records of non-critically ill patients treated with basal insulin prior to admission were identified. After exclusion of records in which a confounding factor influencing insulin dosing was present, 258 patient-admissions over a 3-year time period were included. Multivariate logistic regression was used to analyze the association between adjustments to home insulin total daily dose (TDD) and inpatient glycemic control within the first 48h, adjusting for relevant confounders. On hospital days 1 (HD1) and 2 (HD2), the home insulin TDD was reduced by 43.5% and 23.9%, respectively. Reductions in the home TDD ranging from 10% to 50% were not associated with normoglycemia or hyperglycemia, whereas increases ranging from 10% to 50% were associated with 2-5-fold increased odds of hyperglycemia. For patients with home insulin TDD ≥0.4units/kg/day, a weight-based dose of 0.4-0.6units/kg/day was associated with significantly higher odds of normoglycemia on HD2 (OR 3.99; 95% CI 1.42-11.21) compared to lower doses. Compared to less aggressive increases, home insulin dose increases ranging from 10% to 50% were associated with greater odds of hyperglycemia without increased odds of hypoglycemia during early hospitalization. Weight-based insulin dosing may be a preferred strategy for glycemic control among patients whose home TDD is ≥0.4units/kg/day.

Glycemic Control Reduces Infections in Post-Liver Transplant Patients: Results of a Prospective, Randomized Study.

Previous studies have shown a relationship between glycemic control and posttransplant morbidity. We conducted a prospective randomized controlled trial in postliver transplant patients to evaluate intensive inpatient glycemic control and effects on outcomes to 1 year. A total of 164 patients [blood glucose (BG) >180 mg/dL] were randomized into 2 target groups: 82 with a BG of 140 mg/dL and 82 with a BG of 180 mg/dL. Continuous insulin infusions were initiated and then converted to subcutaneous basal bolus insulin therapy by our glucose management service. The inpatient mean BG level was significantly different (140 group, 151.4 ± 19.5 mg/dL vs 180 group, 172.6 ± 27.9 mg/dL; P < 0.001). Any infection within 1 year occurred in 35 of the 82 patients (42.7%) in the 140 group and 54 of 82 (65.9%) in the 180 group (P = 0.0046). In a time-to-first infection analysis, being in the 140 group resulted in a hazard ratio of 0.54 (95% confidence interval, 0.35 to 0.83; P = 0.004); the difference between the 2 groups was statistically significant at 1 month (P = 0.008). The number with adjudicated transplant rejection was similar between the 2 groups [17 of 82 (20.7%) and 20 of 82 (24.3%) in the 140 and 180 groups, respectively; P = not significant]. Severe hypoglycemia (BG ≤40 mg/dL) occurred in 3 patients (2 in the 140 group and 1 in the 180 group). However, more patients had moderate hypoglycemia (BG, 41 to 70 mg/dL) in the 140 group [27 of 82 (32.9%) vs 10 of 82 (12.2%) in the 180 group; P = 0.003]. Insulin-related hypoglycemia was not associated with the incidence of severe adverse outcomes. Glycemic control of 140 mg/dL safely resulted in a reduced incidence of infection after transplantation compared with 180 mg/dL, but with an increase in moderate hypoglycemia.

Computerized Insulin Order Sets and Glycemic Control in Hospitalized Patients

BackgroundThe purpose of this study was to evaluate the impact of computerized provider order entry subcutaneous insulin order sets on inpatient glycemic control and ordering behavior. MethodsThis was an interrupted time series study of non-intensive care patients at an urban teaching hospital. The primary outcome was proportion of capillary blood glucose in optimal range (4.0-10.0 mmol/L [72-180 mg/dL]) during the 6 months before and after a change to a computerized provider order entry-integrated insulin order set. Secondary outcomes included other measures of glycemia (hyperglycemia [>13.9mmol/L (250 mg/dL)], hypoglycemia [<4.0 mmol/L (72 mg/dL)], severe hypoglycemia [<2.2 mmol/L (40 mg/dL)]) and ordering behavior (use of basal-bolus-correctional insulin regimens). Comparisons of sensitivity-based versus generic correctional scale were also conducted. ResultsA total of 63,393 measurements were obtained from June 2011 to June 2012. Order set usage was limited (51.5%). The weekly proportion of capillary blood glucose within the optimal range was not significantly different after the switch to computerized provider order entry order sets (pre-period: 64.9% vs post-period: 65.3%, P = .996). There were no differences in the proportions of moderate or severe hyperglycemia (pre-period: 10.9% vs post-period: 12.0%, P = .061) and hypoglycemia (pre-period: 1.9% vs post-period: 1.6%, P = .144). However, an increased proportion within the optimal range was seen in those with an order set featuring a sensitivity-based correctional scale versus orders without (65.3% vs 55.0%, P <.001). Increased basal-bolus-correctional ordering was observed after protocol implementation (20.3% vs 23.6%, P <.0001). ConclusionsWith low institutional uptake, computerized insulin order sets did not improve inpatient glycemic control.

Introduction of New South Wales adult subcutaneous insulin-prescribing chart in a tertiary hospital: its impact on inpatient glycaemic control.

Erratic blood glucose levels (BGL) are commonly observed amongst patients with diabetes mellitus during hospital admission. Patients on insulin therapy often do not have their doses titrated adequately by their team doctors during admission, and insulin is well known to be a high-risk medication prone to administration error. To assess the impact of a state-wide adult subcutaneous insulin-prescribing chart (ASCIPC) on glycaemic control and insulin-prescribing pattern in a tertiary hospital. An audit on the clinical records of inpatients who were on subcutaneous insulin therapy in the first week of July 2014 (prior to ASCIPC, n = 56) and in the first week of July 2015 (10 months after introducing ASCIPC, n = 62) was conducted at Liverpool Hospital. Following the introduction of ASCIPC, fewer BGL readings were missed (9.1 vs 11.6%, P = 0.032), and medical officers were more likely to adjust insulin dosage (71.0 vs 42.6%, P = 0.002) when compared to baseline. Glycaemic control improved, with lower mean BGL (9.4 ± 2.0 vs 10.4 ± 2.6 mmol/L, P = 0.021) and a greater proportion of BGL within the normal range of 5-10 mmol/L (56.2 vs 47.7%, P = 0.041). Omission of insulin doses after ASCIPC remained common, with over 40% of patients having at least one dose of insulin omitted during the audit week. Our study showed that the introduction of ASCIPC had positive impacts on glycaemic management for patients on subcutaneous insulin therapy during admission. More work is required to reduce the rate of insulin omission and to improve further glycaemic control for inpatients.

Glycemic Control of Hospitalized Patients at a Medical Center in Taiwan

Achieving the glycemic target is essential to reduce morbidity and mortality in hospitalized patients; however, the status of glycemic control in Taiwan has not yet been evaluated. We conducted a retrospective study to evaluate the status of glycemic control in our hospital. We collected all the point-of-care blood glucose (POC-BG) data generated in general wards and intensive care units (ICUs) between April 2012 and June 2012. Parameters including mean of total POC-BG values, proportion of any POC-BG value less than 70 mg/dL, more than 250 mg/dL and 70 ~ 250 mg/dL, patient-day weighted mean glucose (PDWMG) value and percentage of PDW-MG value more than 180 mg/dL were compared between patients in medical and surgical wards, patients in medical and surgical ICUs, and among patients in each of the medical wards and surgical wards. The mean glucose and PDW-MG values were 192.4 mg/dL and 183.1 mg/dL in the general wards and 196.3 mg/dL and 187.7 mg/dL in the ICUs, respectively. The quality of inpatient glycemic control was poorer for patients in medical wards than in surgical wards, but the difference was smaller between patients in medical and surgical ICUs. The quality of glycemic control among patients in the different medical wards was inconsistent. In summary, the quality of inpatient glycemic control in our hospital was found to be suboptimal as compared with the current guidelines. The development of a strategy for improvement is critical, and the subsequent clinical effectiveness needs further investigation.